Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without

With advances in modern technology, the security of information, including the protection of digital images, is of particular interest. Due to the type of special storage format of images and the fateful dimension of 1D map that have a small secret key space, in this paper, a joint-based encryption technique on the pseudo-random and sophisticated character of hyperchaotic behavior and DNA coding is

Heart disease patients are continuously increasing. The patients face the problem of a delayed diagnosis as the subjects do not undergo routine tests and consult a doctor only after severe symptoms. Most medical expert systems are designed to aid the doctors in making wise decisions and only such data sets exist in the literature. We attack the problem of an early-stage diagnosis that can be done at

This article introduces a robust image watermarking primarily founded on DWT (discrete wavelet transform), BEMD (bi-dimensional empirical mode decomposition), DCT (discrete cosine transform), PSO (particle swarm optimization), and SVD (singular value decomposition). During the process of embedding, 2nd level DWT is used to decompose the cover image into sub-bands. DWT is also used for the decomposition

Every Virtual Reality (VR) experience has to end at some point. While there already exist concepts to design transitions for users to enter a virtual world, their return from the physical world should be considered, as well, as it is a part of the overall VR experience. We call the latter outro-transitions. In contrast to offboarding of VR experiences, that takes place after taking off VR hardware

Haze removal is an interesting topic in multimedia and image processing for many applications. Specially for the automatic piloting of ships, the haze removal technology for aqueous vapour regions plays a key role in safe piloting. However, the existing haze removal methods did not dehaze well for these areas. Based on this motive, this paper presents a new haze removal approach to improve the dehazing

Cardiac arrest is a common issue in Intensive Care Units (ICU) with low survival rate. Deep learning algorithms have been used to predict cardiac arrest unsuccessfully due to low sensitivity and high false alarm rate. The aims of this research are to decrease the false alarm rate and increase sensitivity and specificity. The proposed system is based on Medical Information Mart for Intensive Care (MIMIC-III)

Being able to collect rich morphological information of brain, structural magnetic resonance imaging (MRI) is popularly applied to computer-aided diagnosis of Alzheimer’s disease (AD). Conventional methods for AD diagnosis are labor-intensive and typically depend on a substantial amount of hand-crafted features. In this paper, we propose a novel framework of convolutional neural network that aims at

A computerized bone age assessment requires segmentation of the X-ray carpal bones from other undesired tissue regions. This paper presents segmentation and area measurement of carpal bones in X-ray images. The locally weighted K-means variational level set was applied in segmenting 67 X-ray carpal bone datasets. Dice coefficient and Hausdorff distance measures show mean values above 0.7 and around

In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save

In the forensic investigation, recognizing and locating the multiple diatom objects in an image is a challenging issue due to the interferences of the highly complex backgrounds. To address this issue, a coarse to fine diatom recognition and a localization framework based on the deep learning network is proposed in this paper. Firstly, the diatom images are obtained by performing the anatomic study

Human activity recognition has become one of the most active areas of research in computer vision, due to its increasing demand in many automated monitoring applications such as visual surveillance, human-computer interaction, health care, security systems, and many more. This work aims to introduce an integrated feature descriptor which combines texture feature and shape feature, at multiple orientations

Action recognition in still images is an interesting subject in computer vision. One of the most important problems in still image-based action recognition is the lack of temporal information; At the same time, other existing problems such as cluttered backgrounds and diverse objects make the recognition task more challenging. However, there may be several salient regions in each action image, employing

In the register detection of printing field, a new approach based on Zernike-CNNs is proposed. The edge feature of image is extracted by Zernike moments (ZMs), and a recursive algorithm of ZMs called Kintner method is derived. An improved convolutional neural networks (CNNs) are investigated to improve the accuracy of classification. Based on the classic convolutional neural network (CNN), the improved

Image dehazing is a pre-processing step in computer vision tasks, that has attracted considerable attention from the research community. Existing CNN-based methods ignore haze-related priors and rarely use a coarse-to-fine scheme in a feed-forward architecture to remove haze due to increasing network depth and parameters. This results in sub-optimal dehazing results. To address these problems, a multi-scale

In this work, to overcome the flaws of standard chaotic maps we generated two new cascaded structures, 3D Cubic-Sine and 2D Cubic-Cat chaotic parametric maps, based on a new parameter-varied Cubic map that enhance the chaos complexity and therefore offering a high randomness’s level of the chaotic sequences and a huge key space.To provide optimum security for image transmission, we introduce several

In this paper a novel multikernel deterministic extreme learning machine (ELM) and its variants are developed for classification of non-linear problems. Over a decade ELM is proved to be efficacious learning algorithms, but due to the non-deterministic and single kernel dependent feature mapping proprietary, it cannot be efficiently applied to real time classification problems that require invariant

The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution

Semantic similarity assessment between concepts is an important task in many language related applications. In the past, many approaches to assess similarity of concepts have been proposed by using one knowledge source. In this paper, some limitations of the existing similarity measures are identified. To tackle these problems, we propose an extensive study for semantic similarity of concepts from

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform

Many neighborhood-based clustering algorithms have been proposed to measure the similarity between data points or subclusters with their neighborhood information. However, most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we propose a neighborhood-based three-stage hierarchical clustering algorithm (NTHC) which is robust to the difference. Three concepts

Over the decades, there has been a sustained effort to use fashion as a medium for delivering digital functionality. The goal is to integrate information technology (IT) into clothing to provide users with functions to assist them in their tasks. Regarding the direction of previous efforts, this study developed a multifunctional smart outdoor jacket prototype that senses, recognizes, responds, and

Compared to extractive machine reading comprehension (MRC) limited to text spans, multi-choice MRC is more flexible in evaluating the model’s ability to utilize external commonsense knowledge. On the one hand, existing methods leverage transfer learning and complicated matching networks to solve the multi-choice MRC, which lacks interpretability for commonsense questions. On the other hand, although

Segmentation of the endocardial and epicardial boundaries on 3D cardiac magnetic resonance images plays a vital role in the assessment of ejection fraction, wall thickness, end-diastolic volume, end-systolic volume, and stroke volume. Accurate segmentation is significantly challenged by intensity inhomogeneity artifacts, low contrast, and ill-defined organ/region boundaries. We propose a two stage

Copy-move image forgery is a type of digital image forgery where content is copied and pasted within the same image, either by hiding foreground objects within an image by copying a background region over them or by emphasizing some foreground objects of the image through duplication. To prevent any consequences arising from the consumption of these forged images, many Copy-Move Forgery Detection (CMFD)

Gait recognition from videos is a very important task for surveillance video analysis. Although a number of studies have explored gait recognition models, they lack clarity in the gait contour segmentation, which is an important but difficult step for automatic gait recognition. Most of the gait recognition algorithms use manually segmented gait contours, which is not available in real situations and

In this paper we propose the FD-TR: Feature Detector Based on Scale Invariant Feature Transform and Bidirectional Feature Regionalization for digital image watermarking. The Scale Invariant Feature Transform method is applied to extract keypoints and an Edge and Neighbor Filtering method is proposed to generate the candidate feature points. Then the Bidirectional Feature Regionalization method is proposed

Difference between similar feature points is presented in the fine-grained classification, which depends on discriminative in extremely localized regions. Hence, the accurate localization of discriminative regions is the major challenge found in the fine-grained feature extraction and classification. The patch-based framework has been described to address this issue. The accurate patch localization

The dynamic non-uniform blur caused by Radio Frequency Identification (RFID) multi-label motion seriously affects the identification and location of labels. It is an ill-posed inverse problem for that the blur kernel and sharp image are unknown. The traditional method of removing the blur is very time-consuming. In this work, we propose Multi-scale Recursive Codec Network based on the Authority Parameter

We have developed a parametric model to quantify the Key Quality Indicators which affect video-based Tele-operated Driving (ToD) over a mobile network, as well as their relationship with the network Key Performance Indicators. This model can be easily used to specify Quality of Service policies (e.g. through network slicing) that guarantee the required conditions for remote driving on specific areas

The newest video coding standard, versatile video coding (VVC), has just been published recently. While it greatly improves the performance over the last High Efficiency Video Coding (HEVC) standard, there are still blocking artifacts under the more flexible block partitioning structures. In order to reduce the blocking artifact and improve the quality of the reconstructed video frame, an explicit

Human action recognition is an active research area in computer vision. Although great progress has been made, previous methods mostly recognize actions from depth video sequences at only one scale, and thus they often neglect multi-scale spatial changes that provide additional information in practical applications. In this paper, we present a novel framework with a multi-scale mechanism to improve

Monocular depth estimation is a very challenging task in computer vision, with the goal to predict per-pixel depth from a single RGB image. Supervised learning methods require large amounts of depth measurement data, which are time-consuming and expensive to obtain. Self-supervised methods are showing great promise, exploiting geometry to provide supervision signals through image warping. Moreover

The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold

Context-sensitive graph grammars have been intuitive and rigorous formalisms for specifying visual programming languages, as they are sufficient expressive and equipped with parsing mechanisms. Parsing has been a fundamental issue in the research of context-sensitive graph grammars. However, the existent parsing algorithms are either inefficient or confined to a minority of graph grammars. This paper

Multimodal sentiment analysis is an actively developing field of research. The main research problem in this domain is to model both intra-modality and inter-modality dynamics. However, most of the current work cannot do well with these two aspects of dynamics. In this study, we introduce a novel model to achieve this. The novelty of our model is to represent the asymmetric weights of contexts at a

Detection and recognition of military vehicle from a given image or a video frame with the help of unmanned aircraft system is the major issue which we are concerned about. Vehicle identification and classification from a resource constraint device embedded on an aerial vehicle integrated with an intelligent object detection algorithm, is a big support for defence agency. The vehicle can be controlled

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can

Color music has attracted great interest in real applications. However, the mismatching problem between music and color has not been resolved. This paper proposes a novel mechanism to map the corresponding relation between music and color, which is embedded into a device with a micro-processor to play music color flashing. The proposed mechanism deduces perfect fifth relation among the wavelengths

In the digital transparent world, share the secret messages are challenged one. Visual cryptography (VC) is safer method for information transfer without any distraction by hackers. It is giving more security to the information sharing through digital form. This method hides the messages into images. So intruder cannot understand the distorted image and the data communication become secured. Through

Emotion recognition through speech is one of the fundamental approaches for human interaction. Speech modulations stipulate different emotions and context. In this paper, we propose modified dense convolutional networks (modified DenseNet201) for emotion detection from speech using its paralinguistic features such as vocal tract features. The proposed network performs emotion classification from speech

The usage of productive baseband receivers are portrayed by reasonable computational load is an urgent point in the advancement of transmission frameworks misusing assorted qualities in various spaces.Several research works have been published in the literature ofspace-time block codes multi-carrier code-division multiple-access (STBC MC-CDMA)Systems based on minimum conditional bit error rate Criterion

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle

Multiple-choice questions (MCQs) are used as instrumental tool for assessment, not only in various competitive examinations but also in contemporary information and communications Technology (ICT)-based education, active learning, etc. Therefore, automatic generation of multiple-choice test items from text-based learning material is a truly demanding task in computer aided-assessment. A lot of systems

One of the critical challenges facing 3D video systems and images such as holography lies in their compression technique. High-efficiency video coding (HEVC) has emerged as one of the leading schemes to address this challenge. In this article, a novel method based on wavelet transform is presented to improve HEVC, particularly in digital holography systems (object plane). In this regard, wavelet and

Traditional network data security sharing system ignores that many people share data simultaneously, which leads to poor real-time performance. Therefore, the authors designed a network data security sharing system based on blockchain technology. In the hardware design, the PCI encryption card is used to encode the data. The microprocessor is used to access the system’s external equipment, and DDR-SDRAM

We propose a hybrid grasshopper optimizer to reduce the size of the feature set in the steganalysis process using information theory and other stochastic optimization techniques. This paper results from the stagnancy of local minima and slow convergence rate by the grasshopper algorithm in optimization problems. Therefore, we enhance the grasshopper optimization (GOA) performance with chaotic maps

Visual cryptography is an encryption technique that decomposes secret images into multiple shares. These shares are digitally or physically overlapped to recover the original image, negating the need for complex mathematical operations or additional hardware. There have been many variations of visual cryptography proposed over the years, each addressing different problems or to fulfill different security

Single image dehazing (SID) solves the atmospheric scattering model (ATSM). The ill-defined nature of the SID makes it a challenging problem. The transmission is the prime parameter of ATSM. Hence, accurate transmission is essential for quality of SID. The existing methods of SID estimate the transmission based on priors with strong assumptions (such as dark channel prior). These methods do not recover

The explosive growth in online video streaming presents challenges for video understanding with high accuracy and low computation complexity. Recent methods have realized global video representation without considering the local spatial structures of the videos over time. In this paper, we propose a method called partial channel fusion (PCF), which exploits local spatio-temporal characteristics for

E-healthcare requires communication of patient report to a specialized doctor in a real time scenario. Therefore, any harm to patient medical data can lead to a faulty diagnosis that can be lethal for the patient. To ensure safe and secure communication in E-healthcare framework, an efficient reversible data hiding algorithm in encrypted domain has been proposed in this paper. The proposed algorithm

Quality of Experience (QoE) measures the overall quality of a service from users’ point of view by considering several system, human, and contextual factors. There exist various objective and subjective methods for QoE prediction. Although the subjective approach is more expensive and challenging than the objective approach, QoE’s level can be more accurately determined by a subjective test. This paper

The integration of the Internet of Things in medical systems referred to as the Internet of Medical Things (IoMT), which supports medical events for instance real-time diagnosis, remote monitoring of patients, real-time drug prescriptions, among others. This aids the quality of services provided by the health workers thereby improve patients’ satisfaction. However, the integrity and confidentiality

As a research hotspot in the field of robotics, Simultaneous localization and mapping (SLAM) has made great progress in recent years, but few SLAM algorithms take dynamic or movable targets in the scene into account. In this paper, a robust new RGB-D SLAM method with dynamic area detection towards dynamic environments named GMSK-SLAM is proposed. Most of the existing related papers use the method of

The whole world is facing a health crisis, that is unique in its kind, due to the COVID-19 pandemic. As the coronavirus continues spreading, researchers are concerned by providing or help provide solutions to save lives and to stop the pandemic outbreak. Among others, artificial intelligence (AI) has been adapted to address the challenges caused by pandemic. In this article, we design a deep learning

Image decolorization is to transform a color image into a grayscale image with the preserved contrast and consistent details. It is an important tool in image processing and realistic applications, such as monochrome printing and e-ink display. In this paper, we propose a novel contrast preserving method for image decolorization. Our main contribution is threefold: Firstly, we define a new contrast

Technologies developed in recent years have transformed the way we communicate, collaborate and work. It is therefore necessary to consider them from this point of view in the context of training. Learners evolve in a learning ecosystem that includes the learner himself but also his/her physical and social environment. Learning innovation is no longer about creating new teaching methods, but rather

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively

We propose a new vision-based people-counting method that uses flow analysis with the movement speed of a person to increase the accuracy of people-counting. The proposed method consists of two procedures: simple estimation of foreground movement speed and multiple people detection based on the flow analysis. First, we extract the flow that is generated by the movements of the foreground, and its volume